On Macintosh keyboards it's ⌥⇧-. On the iOS soft keyboard, it's a long-press on the hyphen key. People often point out that various software will turn a double hyphen -- into one by default. We're overflowing with different ways to type an em dash, for those with any curiosity to learn.
I have to be honest. While this is obviously a smart and useful idea, it misses one of the core features of Jev: its confidence scores. Partial confidence could easily be mapped to fractional spaces, using unicode characters like U+2009: THIN SPACE. As it stands, this package is not harnessing the full power of Jev.
important to note that the "confidence" score is... maybe not what people think it is - kind of useless, and just a convenience step from the probabilities.
from the docs: "confidence is a statistic computed from the probability distribution the answer already gives you." [0] I actually encourage people to visit the docs because it has a specific page on this with a little applet to really make this clear.
What else do people think it is? If Typesafe had found a way to measure arbitrary AI results against objective reality (past, future and present) they'd either be making a killing on the stock market or working for the NRO, not publishing that as a confidence value on their API
Well I think there's an expectation that it's similar to the probability score, something that's outputted by the model itself, and so there's some level of "intelligence" (e.g. being able to recognize if the subject matter is relevant to the data it's been trained on, or as an accumulation of errors e.g. it couldn't figure out the question).
Yeah I keep getting this weird sense that Jev is kinda poorly reinventing ML. I guess the graphs don't lie and theoretically I can replace luna with it, but I don't really use luna anyway.
What is the use case for a classifier that works 90% of the time...? I feel like if I'm classifying something, I probably care enough that 90% ain't gonna cut it...
I guess the answer is just agential stuff that effectively gets double checked by the LLM in the driver seat, anyway? That tracks, though it means that jev is mostly just for the people making harnesses. Which is all of us but still!
I think the argument would be that the classifiers of classic ML can be very useful and that Jav is a geenral purpose classifier you can just use that doesn't need to be trained per-task.
I mean when you get your bloodwork done to check for an illness, the test you get will give the right result 90% of the time - and depending on the result, you doc might order more tests, which could be more expensive but no mrpe reliable than the first - but they are going to be statistically independent, and after 2 more, he can be 99.9% sure.
Which begs the question, can Jev retest until it gets the right result? Can it tell how corellated two of its results are? 90% correct makes for a wonderful iterator, but a poor oracle.
Yup, you articulated what I was trying to say but much more clearly (thanks!). I suppose I haven't tried just setting n=3 or something, but presumably the Jev docs would mention that if it were enough to get it from 1 figure to 2 (i.e. 90% -> 99%). I agree that there are places where 90% certainty can help, but it really needs to be an agential system; when a doctor runs more tests, they are experimentally engaging with empirical reality in a context-appropriate way.
I guess, in the end: I think it'll end up being fantastically useful for artificial engineers with their vastly superior ability to keep track of fine details and rapidly context switch, but fairly niche for any of us organic engineers that are left.
All that doesn't apply to low stakes stuff like games, though -- can't wait for the first truly open world game, NGL. A silver lining to the cobalt cloud?
Be more specific. Is he a bad person for having an amateur interest in mathematics, for exploring that interest through language models, or for writing a blog post about his experience?
That's an excellent question for the maintainers of the MacPorts project! It can supplement the very clear and polite answer you've already received from a maintainer of Homebrew.
It kind of is an explanation though - the explanation is that they believed the stochastic parrot / "just" next token prediction nonsense, and that those are actually not true.
You can ask for a deeper explanation of why they aren't true I guess.
I'm just surprised that so many intellectuals on HN hang on to false models of reality for so long after that reality has been demonstrably destroyed. It's not humble at all. In fact it's the opposite, completely arrogant and stubborn. It's been obvious and demonstrated at least since the end of 2025 for anybody that used LLMs at any capacity without dismissing them. If you are still surprised that your model of reality doesn't hold up, then someone needs to bluntly tell you what's wrong at the core of your being.
Notice the original comment is asking people to validate their false premise about next token prediction.
The deeper subtext of the original comment is that they are surprised that there's dissonance from observed reality and this false premise that they have convinced themself is true.
I'm explaining that dissonance because it doesn't matter what the actual mechanism is if they are still working with their false premise. The dissonance exists because they, without evidence and a very weak understanding of how LLMs work, believed an oversimplification and meme about them being stochastic parrots. Here's a tip: Just because you hear something repeated over and over on social media, doesn't mean it's true, or at the very least: you don't need to take it literally to the point where it conflicts with demonstrated reality.
It is deeply disturbing that such a large cohort of HN writers and redditors exemplify such stubbornness, because I must imagine that some of this cohort hold real positions of responsibility within society. If you can't get this simple thing right about reality, I firmly believe much of your model of reality is wrong and you should have no business shaping society.
Another comment to the original comment frames it perfectly: "At what point do you challenge your own assumptions?"
The author of the original comment has demonstrated no progress towards making this trivial act of self reflection. It's straight up intellectual dishonesty, the opposite of how you're framing it.
Their judgment in all other matters must be questioned as well. I am alarmed that I have to participate in the same reality and be affected by such people that can't seem to get it together.
So yes, it is derision and sometimes that's called for.
With an open-weights model, anyone with the hardware to run the model can act as a provider. The median provider pays nothing for the model, so they do not contribute to the cost of training it.
Are you sincerely advocating that Wikipedia should never delete anything, regardless of notability or verifiability? Do you think that would make it better or worse?
But what _is_ a "Text User Interface"? Google Images just returns what is being discussed here: "GUIs" that run in some kind of text mode. And to me, that's also what a TUI is.
A more textually oriented environment (like a normal Unix shell) is, in my experience, usually referred to as a CLI: Command Line Interface.
I did find an interesting hybrid in the Pi coding agent: it seems to leverage the normal terminal scrollback, while still enhancing it with things like transient input fields and status lines, so that it can display those without cluttering scrollback.
That's pretty much the distinction. A CLI is stream-oriented, may accept user input from standard in and/or write to standard out with buffered I/O; a TUI is full-screen, interactive (responds immediately to keystrokes), and uses text characters to represent visual elements.
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